Use of the nominal group technique to identify stakeholder priorities and inform survey development: an example with informal caregivers of people with scleroderma
Bibliographic record
Abstract
OBJECTIVES: The nominal group technique (NGT) allows stakeholders to directly generate items for needs assessment surveys. The objective was to demonstrate the use of NGT discussions to develop survey items on (1) challenges experienced by informal caregivers of people living with systemic sclerosis (SSc) and (2) preferences for support services. DESIGN: Three NGT groups were conducted. In each group, participants generated lists of challenges and preferred formats for support services. Participants shared items, and a master list was compiled, then reviewed by participants to remove or merge overlapping items. Once a final list of items was generated, participants independently rated challenges on a scale from 1 (not at all important) to 10 (extremely important) and support services on a scale from 1 (not at all likely to use) to 10 (very likely to use). Lists generated in the NGT discussions were subsequently reviewed and integrated into a single list by research team members. SETTING: SSc patient conferences held in the USA and Canada. PARTICIPANTS: Informal caregivers who previously or currently were providing care for a family member or friend with SSc. RESULTS: A total of six men and seven women participated in the NGT discussions. Mean age was 59.8 years (SD=12.6). Participants provided care for a partner (n=8), parent (n=1), child (n=2) or friend (n=2). A list of 61 unique challenges was generated with challenges related to gaps in information, resources and support needs identified most frequently. A list of 18 unique support services was generated; most involved online or in-person delivery of emotional support and educational material about SSc. CONCLUSIONS: The NGT was an efficient method for obtaining survey items directly from SSc caregivers on important challenges and preferences for support services.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.164 | 0.179 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".